ORIGINAL RESEARCH ARTICLE
ENVIRONMENTAL SCIENCE
published: 20 October 2014
doi: 10.3389/fenvs.2014.00044
Future rainfall variations reduce abundances of
aboveground arthropods in model agroecosystems with
different soil types
Johann G. Zaller 1*, Laura Simmer 1 , Nadja Santer 1 , James Tabi Tataw 1 , Herbert Formayer 2 ,
Erwin Murer 3 , Johannes Hösch 4 and Andreas Baumgarten 4
1
2
3
4
Department of Integrative Biology and Biodiversity Research, Institute of Zoology, University of Natural Resources and Life Sciences Vienna, Austria
Department of Water-Atmosphere-Environment, Institute of Meteorology, University of Natural Resources and Life Sciences Vienna, Austria
Institute of Land and Water Management Research, Federal Agency for Water Management, Petzenkirchen, Austria
Division for Food Security, Institute of Soil Health and Plant Nutrition, Austrian Agency for Health and Food Safety (AGES), Vienna, Austria
Edited by:
Vimala Nair, University of Florida,
USA
Reviewed by:
Astrid Rita Taylor, Swedish
University of Agricultural Sciences,
Sweden
Holger Hoffmann, Leibniz
Universität Hannover, Germany
Todd Z. Osborne, University of
Florida, USA
*Correspondence:
Johann G. Zaller, Department of
Integrative Biology and Biodiversity
Research, Institute of Zoology,
University of Natural Resources and
Life Sciences Vienna, Gregor
Mendel Straße 33, A-1180 Vienna,
Austria
e-mail: [email protected]
Climate change scenarios for Central Europe predict less frequent but heavier rainfalls and
longer drought periods during the growing season. This is expected to alter arthropods in
agroecosystems that are important as biocontrol agents, herbivores or food for predators
(e.g., farmland birds). In a lysimeter facility (totally 18 3-m2 -plots), we experimentally
tested the effects of long-term current vs. prognosticated future rainfall variations (15%
increased rainfall per event, 25% more dry days) according to regionalized climate change
models from the Intergovernmental Panel on Climate Change (IPCC) on aboveground
arthropods in winter wheat (Triticum aestivum L.) cultivated at three different soil types
(calcaric phaeozem, calcic chernozem and gleyic phaeozem). Soil types were established
17 years and rainfall treatments 1 month before arthropod sampling; treatments were fully
crossed and replicated three times. Aboveground arthropods were assessed by suction
sampling, their mean abundances (± SD) differed between April, May and June with
20 ± 3 m−2 , 90 ± 35 m−2 , and 289 ± 93 individuals m−2 , respectively. Averaged across
sampling dates, future rainfall reduced the abundance of spiders (Araneae, −47%), cicadas
and leafhoppers (Auchenorrhyncha, −39%), beetles (Coleoptera, −52%), ground beetles
(Carabidae, −41%), leaf beetles (Chrysomelidae, −64%), spring tails (Collembola, −58%),
flies (Diptera, −73%) and lacewings (Neuroptera, −73%) but increased the abundance of
snails (Gastropoda, +69%). Across sampling dates, soil types had no effects on arthropod
abundances. Arthropod diversity was neither affected by rainfall nor soil types. Arthropod
abundance was positively correlated with weed biomass for almost all taxa; abundance of
Hemiptera and of total arthropods was positively correlated with weed density. These
detrimental effects of future rainfall variations on arthropod taxa in wheat fields can
potentially alter arthropod-related agroecosystem services.
Keywords: agroecology, climate change ecology, precipitation patterns, soil types, aboveground invertebrates,
lysimeter, winter wheat, animal-plant interactions
INTRODUCTION
Climate change will very likely cause a seasonal shift in precipitation in Central Europe resulting in less frequent but more extreme
rainfall events during summer but increased precipitation during winter (IPCC, 2007, 2013). Regionalisations of these climate
models for eastern parts of Central Europe prognosticate little
changes or even slight decreases in annual rainfall amounts until
2100 (Eitzinger et al., 2001; Kromp-Kolb et al., 2008). Indeed, so
far for eastern Austria no change in total yearly precipitation was
measured during the last decades (Formayer and Kromp-Kolb,
2009). The direction, magnitude and variability of such changes
in precipitation events and their effects on ecosystem functioning
will depend on how much the change deviates from the existing
variability and the ability of ecosystems and inhabiting organisms
to adapt to the new conditions (Beier et al., 2012).
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In many natural and agriculturally managed ecosystems
arthropods are the most abundant and diverse group of animals
(Altieri, 1999; Speight et al., 2008). Abundances of epigeic arthropods in an arable field can reach thousands of individuals m−2
comprising hundreds of species (Romanowsky and Tobias, 1999;
Östman et al., 2001; Pfiffner and Luka, 2003; Batary et al., 2012;
Frank et al., 2012; Querner et al., 2013). These arthropods play
important ecological roles as herbivores and detritivores (Seastedt
and Crossley, 1984), are valued for pollination, seed dispersal and
predation (Steffan-Dewenter et al., 2001), are important predators and parasitoids (Thies et al., 2003; Drapela et al., 2008; Zaller
et al., 2008a, 2009) and are a food source for many vertebrates and
invertebrates (Price, 1997; Brantley and Ford, 2012; Hallmann
et al., 2014). As arthropods can have a strong influence on nutrient cycling processes (Seastedt and Crossley, 1984), they are also
October 2014 | Volume 2 | Article 44 | 1
Zaller et al.
very important for ecosystem net primary production (Abbas and
Parwez, 2012). Predicted longer drought intervals between rainfall events will increase drought stress for crops while changes
in the amount and timing of rainfall will affect yields and the
biomass production of crops (Eitzinger et al., 2001; Alexandrov
et al., 2002; Thaler et al., 2008). These changes in vegetation structure and quality will also affect associated arthropods (Andow,
1991). Moreover, it has also been shown that changes in the magnitude and variability of rainfall events is likely to be more important for arthropods than changes in annual amounts of rainfall
(Curry, 1994; Speight et al., 2008; Singer and Parmesan, 2010).
Most studies investigating potential effects of climate change on
arthropods have focused on the effects of changes in atmospheric
CO2 concentrations or temperature rather than precipitation
(e.g., Cannon, 1998; Andrew and Hughes, 2004; Hegland et al.,
2009; Hamilton et al., 2012). However, changes in variations of
rainfall are likely to have a greater effect on species’ distributions
than are changes in temperature, especially among rare species
(Elmes and Free, 1994).
Surprisingly, very few studies investigated the effects of different rainfall variations on aboveground arthropod abundance
in arable agroecosystems, although arable land is ecologically
important in terms of its diverse arthropod fauna (Frampton
et al., 2000; Tscharntke et al., 2005; Drapela et al., 2008) and
its interaction with natural ecosystems in a landscape matrix
(Tscharntke and Brandl, 2004; Frank et al., 2012; Balmer et al.,
2013; Coudrain et al., 2014). Results from studies investigating
the effects of rainfall variations on arthropods are not consistent ranging from increased spider activity to a reduced activity
of Collembola under reduced rainfall (Lensing et al., 2005) while
others showed little influence of rainfall on spiders (Buchholz,
2010). It also appears that even short rainfall events in spring
can influence various groups of farmland arthropods for the
following months (Frampton et al., 2000).
To the best of our knowledge, no study assessed the effects of
rainfall variations on arthropods in wheat, one of the most important cereal crops worldwide. Moreover, experiments studying
the effects of precipitation on ecosystems are usually conducted
at different locations with different soil types, thus confounding location with soil types and making it impossible to test
to what extent soil types can potentially buffer rainfall variations on ecosystem processes (Beier et al., 2012). The few studies
investigating arthropod abundance in different soil types found
a significant difference in soil fauna abundance and diversity
(Loranger-Merciris et al., 2007) or invertebrate community composition between different soil types (Ivask et al., 2008; Tabi Tataw
et al., 2014).
Hence, the objectives of the current study were: (1) To examine
effects of different rainfall variations on the abundance of aboveground arthropods in winter wheat, (2) to assess to what extent
different soil types alter potential responses of aboveground
arthropods to rainfall variations. The investigations were based
on the hypotheses that differences in the amount and variability of rainfall alter the structure of winter wheat stands by either
affecting growth of crops and/or weeds (Porter and Semenov,
2005) and consequently affecting the abundance and diversity of
arthropods (Duelli and Obrist, 2003; Menalled et al., 2007). As
Frontiers in Environmental Science | Agroecology and Land Use Systems
Future rainfall and agroecosystem arthropods
the composition of arthropod communities changes during the
season we expected that different arthropod taxa would be differently affected by rainfall variations (Price et al., 2011). Moreover,
different moisture sensitivities/drought tolerances of arthropod
taxa (Finch et al., 2008) will be affected by soil types with different water holding capacities and soil types will also modify the
growth and structure of vegetation that will interact with rainfall
variations in affecting arthropods.
MATERIALS AND METHODS
STUDY SITE
The experiment was carried out in the lysimeter experimental facility of the Austrian Agency for Health and Food Safety
(AGES), in Vienna, Austria (northern latitude 48◦ 15 11 , eastern longitude 16◦ 28 47 ) at an altitude of 160 m above sea
level. The facility is located in a transition area of the Western
European oceanic (mild winters, wet, cool summer) and the
Eastern European continental climatic area (cold winters, hot
summers) ecologically referred to as the Pannonium region.
Long-term mean annual precipitation at this site is 550–600 mm
at a mean air temperature of 9.5◦ C (Danneberg et al., 2001).
The lysimeter facility was established in 1995 and consists of
18 cylindrical vessels made of stainless steel each with a surface
area of 3.02 m2 and a depth of 2.45 m (Figure 1). The lysimeters are arranged in two parallel rows with nine lysimeter plots in
each row; one row was subjected to current rainfall the other row
to prognosticated rainfall. Within each row three soil types were
randomized to ensure replicates of each soil type in each row (see
below for more details on treatment factors); each treatment was
replicated three times (n = 3).
EXPERIMENTAL TREATMENTS
SOIL TYPES
In 1995, the lysimeters were filled with three different soil types
representing around 80% of the agriculturally most productive
FIGURE 1 | Experimental winter wheat plots containing three different
soil types (calcaric phaeozem, calcic chernozem, gleyic phaeozem)
subjected to long-time current prognosticated rainfall variations
according to regionalized climate change models.
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Zaller et al.
area in Austria (region Marchfeld; east of Vienna, Austria): calcaric phaeozem (S), calcic chernozem (T), and gleyic phaeozem
(F; soil nomenclature after World Soil Classification, FAO, 2002).
The soil material was carefully excavated from their native sites in
10 cm layers and filled into the lysimeter vessels retaining their
original bulk densities of 1.4 g cm−3 (Danneberg et al., 2001).
See Tabi Tataw et al. (2014) for further details on the soil characteristics. Briefly, the calcic chernozem and the calcaric phaeozem
have a fully developed AC-profile, emerging from carbonate-fine
siliceous material. The thickness of the A horizon is at least 30 cm,
the humus form is mull with both 4.9% humus content (Nestroy
et al., 2011). The calcic chernozem is moderately dry, the calcaric
phaeozem is dry; both soil types consist of fine sediment to silt
fine sand (Danneberg et al., 2001). The gleyic phaeozem is a soil
of former hydromorphic sites with 2.1% humus content as mull;
the fully developed AC-profile and the thickness of the A-horizon
is at least 30 cm thick (Nestroy et al., 2011). This gleyic phaeozem
is well supplied with water and consists of fine sediment to silt
fine sand; its high lime content, gives this soil type neutral to
slightly alkaline pH. Mean profile water contents are 375, 595, and
550 mm for S, T, and F soil, respectively.
RAINFALL SCENARIOS
Starting in 2011, the lysimeters were subjected to two rainfall
regimes, one based on past local observations (“curr. rainfall”)
and one based on a regionalization of the IPCC 2007 climate
change scenario A2 for the period 2071–2100 (“progn. rainfall”; IPCC, 2007). Both the current and the future precipitation variations were calculated using the software LARS-WG
(Version 3.0; Semenov and Barrow, 2002). In contrast to classic approaches using directly the projected climate time series as
model input our approach with LARS-WG used only the delta
values (Hoffmann and Rath, 2012, 2013). The current longterm rainfall variations was based on the precipitation amount
and frequency for a location in about 10 km distance from the
study site (village of Großenzersdorf) between the years 1971–
2000. The future rainfall scenario for the year 2071–2100 is
based on the local climatology and the climate change signal
from the mean of the regional climate model scenarios from the
EU-project ENSEMBLES (Christensen and Christensen, 2007).
This stochastic weather generator LARS-WG was used to transfer the derived local climate change signals to daily precipitation
rates. To exclude natural rainfall the lysimeters were covered
with a 5 m high roof of transparent plastic foil from March
until December in each year, all sidewalls were open allowing
ventilation and free movement of animals (Figure 1). During
winter the facility was uncovered and all lysimeters received natural precipitation. Rainfall amounts (tap water) according to the
model calculations were applied to nine lysimeters in a row using
an automatic sprinkler system. Rainfall treatments started on
22 March 2012. Until the last arthropod sampling on 18 June
2012 the curr. rainfall plots received 156.4 mm and the progn.
rainfall plots 136.3 mm irrigation water (−13% less amount of
rain). Averaged over the study period, the curr. rainfall plots
received 3.7 mm per rain event vs. 3.2 mm per event for the progn.
rainfall plots (13% difference); progn. rainfall had 25% more
dry days than curr. rainfall treatments (Figure 2A). Irrigation
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Future rainfall and agroecosystem arthropods
FIGURE 2 | Amount of applied rainfall applied onto treatment plots (A)
and mean air temperature (B) during the course of the experiment.
was always performed in early morning at low sunlight; side
walls of the transparent cover were automatically closed during irrigation. Weather stations (Delta-T Devices, Cambridge,
UK) were installed between and outside of the lysimeters for
monitoring air temperature (Figure 2B), wind speed and direction, global radiation and rainfall. Soil matric potential (ψm,
also called soil water potential) was measured using three pF
sensors per lysimeter installed in 10 cm depth (ecoTech UmweltMesssysteme GmbH, Bonn, Germany). The soil matric potential
was automatically measured every 15 min and represents the pressure it takes to pull water out of soil and increases as the soil
gets drier. Technically the pF sensor measure heat capacity in a
porous ceramic tip that contains a heating element and temperature sensors. The correlations of pF values and measured heat
capacity is achieved by a sensor-specific calibration curve (www.
ecotech-bonn.de/en/produkte/Bodenkunde/pF-meter.html). The
matric potential changes with the soil water content and commonly varies between different soil types. Soil matric potential is
usually expressed in pF units which is the log of the soil tension
in hPa (e.g., log of 10,000 hPa is equal to pF = 4). Daily pF values were calculated by averaging the individual readings of each
lysimeter. Field capacity of soil types was pF = 1.8, permanent
wilting point for crops pF = 4.2.
CROP WHEAT
Winter wheat (Triticum aestivum L. cv. Capo) was sown at a density of 400 seeds m−2 on 11 October 2011 after the precrop white
mustard. Weeds in the treatment plots were controlled by spraying a mixture of the herbicides Express-SW (active ingredient:
tribenuronmethyl; Kwizda Agro, Vienna, Austria) at 25 g ha−1 ,
Starane XL (a.i.: fluroxypyr and florasulam; Dow AgroSciences,
Indianapolis, IN, USA) at 750 ml ha−1 and water at 300 l ha−1 on
30 March 2012. Fertilization was applied according to recommendations for farmers after soil analyses (Table 1).
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Zaller et al.
Future rainfall and agroecosystem arthropods
Table 1 | Fertilization of winter wheat crops in lysimeter plots with
STATISTICAL ANALYSES
different soil types (S —calcaric phaeozem, F—gleyic phaeozem,
First, all measured parameters were tested for normal distribution
and variance homogeneity using the Kolmogorov-Smirnov-Test
and Levene-Test, respectively. The two parameters that did not
meet the requirements of parametric statistics, Hemiptera and
total individuals from the May sampling, were Boxcox transformed. Secondly, for all arthropod abundance parameters,
repeated measurement analysis of variance (ANOVA) with the
factors Rainfall (two levels: longtime current rainfall variations
vs. prognosticated rainfall variations), Soil type (three levels: F,
S, and T soils) and Sampling date (three dates: April, May, June
sampling) were conducted. Additionally, to test for treatment
effects at each sampling date separately, two-factorial ANOVAs
with the factors Rainfall and Soil type and their interactions were
conducted for arthropod taxa and for soil pF values. As a measure of community diversity the Simpson and the Shannon index
were calculated and also tested with a two-factorial ANOVA for
each sampling date separately (Rosenzweig, 1995). Pearson correlations were performed between arthropod abundance, crop
height, crop and weed biomass and weed abundance. All statistical analyses were performed using the freely available software
“R” (version 3.0.2; R Core Team, 2013). Statistical results with
P > 0.50 < 0.10 were considered marginally significant. Values
within the text are means ± SD.
T—calcic chernozem).
Fertilizer type
Fertilizer amount
Date
per soil type
(kg ha−1 )
S
F
T
P2 O5 –Triplesuperphosphate
0
55
55
11 October 2011
K2 O–Kali 60
40
0
0
11 October 2011
N–NAC (Nitramoncal 27%)
25
40
40
08 March 2012
N–NAC (Nitramoncal 27%)
30
40
40
12 April 2012
N–NAC (Nitramoncal 27%)
35
50
50
16 May 2012
Wheat growth was measured from the soil surface to the tip
of the spike on 10–15 marked crop plants per lysimeter around
the arthropod sampling dates (see below). Additionally, the number of weed individuals per lysimeter (weed density) was counted
during these dates. Lysimeters were harvested on 5 July 2012
by cutting all vegetation (winter wheat and weeds) by hand at
5 cm above surface. Crop and weed plants were separated, crop
plants devided in straw and spikes and everything was weighed
after drying at 50◦ C for 48 h. In order to avoid boundary effects
all measurements on crops were conducted in the central area
of each lysimeter up to 20 cm distance from the edge of each
lysimeter.
ARTHROPOD SAMPLING
All arthropods dwelling on the soil surface and on the vegetation
in each of the 18 lysimeters were collected using a commercial garden vacuum (Stihl SH 56-D, Dieburg, Germany) equipped with
an insect sampling net. For sampling, the suction tube was carefully moved between the crop plants across the lysimeter area in
order to avoid that the sampling efficiency is too much influenced by vegetation structure, height and density (Southwood,
1978; Brook et al., 2008). To impede the escape of the arthropods, a 1 m high barrier made of plastic film was attached to
the borders of the lysimeter vessels. Suction sampling was performed for 5 min in each lysimeter; afterwards, each plot was
thoroughly inspected for another 20 min for remaining arthropods. This sampling procedure was performed on April 24–25,
May 22–23, and June 19, 2012. Air temperature during arthropod sampling dates was on average 18.2◦ C on the first sampling
event, 23.3◦ C on the second, and 30.4◦ C on the third sampling event (Figure 2B). Sampling was carried out only when
the vegetation and soil surface was dry. After collection, the
arthropods were sorted out, cleaned from attached soil, preserved in 80% ethylene alcohol and identified at the level of
taxonomic order or families (Bellmann, 1999; Bährmann and
Müller, 2005). Taxa with less than 0.3 individuals m−2 were
lumped together in a group of rare individuals. Arthropod abundance was expressed in individuals m−2 and relative abundance
of the identified groups to the arthropod community present in
each lysimeter was calculated in percentage based on the m−2
values.
Frontiers in Environmental Science | Agroecology and Land Use Systems
RESULTS
Soil matrix potential was significantly affected by rainfall (P <
0.001) and soil types (P < 0.001; rainfall × soil type interaction:
P < 0.001) with sandy soils showing the lowest and F and T soil
the highest pF values under both rainfall treatments (Figure 3).
Arthropod abundances differed highly significantly between
sampling dates; rainfall variations significantly affected arthropod
abundances at different sampling dates (i.e., rainfall × sampling
date interaction; repeated measures ANOVA, Table 2, Figure 4).
Averaged across rainfall variations and soil types total arthropod abundance in April was 20.38 ± 3.24 m−2 , in May 89.62 ±
34.74 m−2 and in June 289.23 ± 92.84 m−2 (Figure 4). Overall,
Hymenoptera was the dominant order in April; Hemiptera,
Hymenoptera and Acari were dominant in May and Hemiptera
were the most dominant group in June; especially the abundance
of Hemiptera, Collembola and Acari increased from April to
June.
When analyzing the arthropod abundances separately for each
sampling date using Two-Way ANOVAs, prognosticated rainfall
in April significantly reduced abundances of Gastropoda by 69%
and of Auchenorrhyncha by 61% (Table 3, Figure 4). In May,
prognosticated rainfall significantly reduced Collembola by 53%,
Diptera by 59%, Neuroptera by 73%, and Saltatoria by 70%
(Table 3, Figure 4). In April and May, soil types had no effect
of the abundance of arthropods (except for the group of not
determinable arthropods; Table 3). In June, prognosticated rainfall significantly reduced Araneae by 56%, Auchenorrhyncha by
47%, Coleoptera, and Collembola each by 62%, Chrysomelidae
by 66%, Diptera by 77%, and total individuals by 61% (Table 3,
Figure 4). All other arthropod taxa were not affected by rainfall.
In June, soil types had no effect on arthropod abundance except
for Auchenorrhyncha (Table 3).
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Zaller et al.
Future rainfall and agroecosystem arthropods
FIGURE 3 | Mean soil matric potential in pF units in winter wheat cultivated under current (A) and prognosticated rainfall variations (B) at the
different soil types calcaric phaeozem (S), gleyic phaeozem (F) and calcic chernozem (T).
Table 2 | Summary of repeated measurement ANOVA results of the
influence of rainfall patterns (current and prognosticated rainfall), soil
types (calcaric phaeozem, calcic chernozem, and gleyic phaeozem)
and sampling date (April, May, June 2012) on total abundance of
arthropods in winter wheat.
Factor
F
P
Rainfall
4.36
0.059
Soil type
0.04
0.961
20.87
<0.001
Sampling date
Rainfall × Soil type
1.00
0.398
Rainfall × Sampling date
6.33
0.006
Soil type × Sampling date
0.39
0.815
Rainfall × Soil type × Sampling date
0.44
0.776
Significant effects are in bold.
Considering the relative abundance (i.e., percentage contribution to arthropod community) of the identified arthropod groups
for each sampling date, rainfall variations significantly affected
Collembola (P = 0.036) and Neuroptera (P = 0.041) in May and
Diptera (P = 0.041) in June; with the exception of the relative
abundance of rare individuals in April (P = 0.027) the composition of arthropod communities was not affected by soil types
(Figure 4).
Across sampling dates, absolute abundance of Araneae
(−43%),
Coleoptera
(−48%),
Carabidae
(−41%),
Chrysomelidae (−64%), Collembola (−58%), Diptera (−75%),
Auchenorrhyncha (−39%), and Neuroptera (−73%) were
significantly reduced under prognosticated rainfall, also total
arthropod abundance were marginally significantly lower under
prognosticated rainfall than under current rainfall (Table 3,
Figure 5). Only the abundance of Gastropoda increased by
69% in the prognosticated rainfall compared to current rainfall
(Figure 6). There was no effect of soil types on any of the identified arthropod groups across sampling dates (Table 3, Figure 6).
Considering the relative abundances across sampling dates, only
the relative abundance of Diptera (P = 0.027) and Gastropoda
(P = 0.031) were significantly affected by rainfall; soil types only
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FIGURE 4 | Mean absolute (A) and relative (B) abundance of arthropods
per m2 at the three sampling dates in winter wheat cultivated under
current and prognosticated rainfall variations at the different soil types
calcaric phaeozem (S), gleyic phaeozem (F) and calcic chernozem (T).
significantly affected the relative abundance of rare individuals
(P = 0.010). Hemiptera showed the highest relative abundance
in all fields (Figure 6). Rainfall variations and soil types had no
effect on the diversity indices of arthropod communities (data
not shown).
October 2014 | Volume 2 | Article 44 | 5
0.156
Frontiers in Environmental Science | Agroecology and Land Use Systems
1.000
0.000
0.268
Saltatoria
Thysanoptera
Total individuals
0.614
1.000
0.337
0.457
0.271
–
0.721
0.655
0.044
1.000
0.208
0.023
0.574
0.552
0.247
2.250
2.967
2.333
–
1.233
1.609
3.792
0.250
2.268
2.583
1.000
2.758
1.000
1.050
0.270
0.072
0.673
F
0.590
0.785
0.148
0.090
0.139
–
0.326
0.240
0.053
0.783
0.146
0.117
0.397
0.103
0.397
0.380
0.768
0.931
0.529
P
Soil types
0.237
0.111
12.893
1.393
2.128
7.111
0.563
1.267
3.018
0.305
0.639
0.152
5.831
6.019
0.125
3.559
0.041
0.000
2.043
F
P
0.636
0.744
0.004
0.261
0.170
0.021
0.468
0.282
0.108
0.591
0.440
0.704
0.032
0.030
0.712
0.084
0.843
1.000
0.178
Rainfall
0.600
1.192
1.750
1.877
1.340
0.778
7.000
0.616
0.031
1.622
1.680
4.581
1.241
2.480
1.000
0.912
0.985
3.276
0.228
F
0.565
0.337
0.215
0.195
0.298
0.481
0.010
0.556
0.969
0.238
0.227
0.033
0.324
0.125
0.397
0.428
0.402
0.073
0.800
P
Soil types
May sampling
6.612
3.499
0.098
4.440
4.500
0.143
3.879
1.727
10.670
0.000
4.403
1.333
11.794
10.219
15.591
0.966
21.519
11.927
0.298
F
P
0.010
0.024
0.086
0.760
0.057
0.055
0.076
1.319
1.390
1.908
0.722
1.000
0.072
0.712
0.190
4.227
0.906
0.342
2.083
0.240
1.750
2.187
0.490
3.227
0.056
0.234
F
0.930
0.304
0.286
0.191
0.506
0.397
0.990
0.830
0.041
0.430
0.717
0.167
0.791
0.215
0.155
0.624
0.075
0.946
0.795
P
Soil types
0.213
0.007
1.000
0.058
0.271
0.005
0.008
0.002
0.345
0.001
0.005
0.595
Rainfall
June sampling
2.626
2.691
2.701
3.641
–
4.196
0.019
8.119
0.152
2.495
0.107
13.945
13.750
16.248
6.169
14.757
12.844
0.992
F
P
3.031
2.161
3.665
0.323
–
0.036
1.111
2.016
1.570
0.026
3.663
0.426
2.919
2.624
1.518
2.999
0.367
0.347
F
0.086
0.158
0.057
0.730
–
0.965
0.361
0.176
0.248
0.975
0.057
0.663
0.093
0.113
0.258
0.088
0.700
0.714
P
Soil types
Detailled results in Table 2
0.131
0.127
0.126
0.081
–
0.063
0.894
0.015
0.704
0.140
0.749
0.003
0.003
0.002
0.029
0.002
0.004
0.339
Rainfall
Across dates
– No data available for this date.
using repeated measurement ANOVAs. Significant effects are in bold.
No interaction between rainfall patterns and soil type were detected (except Saltatoria in May P = 0.030). Data for individual sampling dates were analyzed using two ANOVAs, data across dates were analyzed
0.590
Rare individuals
Not determinable
1.333
0.133
Hymenoptera
Opiliones
0.211
Auchenorrhyncha
–
5.042
Heteroptera
Neuroptera
1.768
0.000
Hemiptera
2.286
6.750
Diptera
0.865
Collembola
Gastropoda
0.371
0.333
Chrysomelidae
0.187
0.067
1.960
4.050
Coleoptera
1.000
0.281
P
Carabidae
1.274
0.000
Araneae
Rainfall
Acari
F
Taxa
Arthropod
April sampling
chernozem and gleyic phaeozem) on abundance of arthropods in winter wheat at different sampling dates.
Table 3 | Summary of ANOVA results of the influence of rainfall patterns (long time current vs. prognosticated rainfall patterns) and different soil types (calcaric phaeozem, calcic
Zaller et al.
Future rainfall and agroecosystem arthropods
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Zaller et al.
Future rainfall and agroecosystem arthropods
DISCUSSION
FIGURE 5 | Mean absolute (A) and relative (B) abundance of
arthropods per m2 across the three sampling dates (April, May, June)
in winter wheat cultivated under current and prognosticated rainfall
variations at the different soil types calcaric phaeozem (S), gleyic
phaeozem (F) and calcic chernozem (T).
Wheat height was across sampling dates not affected by
rainfall but significantly affected by soil types with lowest
heights in the S soils and similarly high wheat plants in F and
T soils (significant rainfall × soil type interaction; Table 4).
Wheat straw biomass across sampling dates was significantly
affected by rainfall and soil types (significant rainfall × soil
type interaction; Table 4). Weed abundance across sampling
dates was marginally significantly affected by rainfall variations
and highly significantly affected by soil types (no rainfall ×
soil types interaction; Table 4). Weed biomass across sampling
dates was only significantly affected by soil types with lowest
weed biomass values in F soils and highest weed biomass in
S soils (Table 4). Arthropod abundance was unrelated to winter wheat straw biomass (Table 5) wheat height or weed abundance (data not shown). However, abundances of Acari, Araneae,
Collembola, Diptera, the group of not determinable arthropods
and Thysanoptera was positively correlated with weed biomass
(Table 5).
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Results of this study show substantial reductions in the abundances of various arthropod groups but no changes on the
diversity of arthropod communities under rainfall variations
prognosticated for the years 2071–2100. Given the average 45%
reduction of total arthropod abundance under prognosticated
rainfall means that instead of 86 m−2 only 48 m−2 arthropod
individuals would be inhabiting these wheat agroecosystems.
Arthropod abundance data from the current study fit well with
those from a conventional cereal field in Denmark also assessed
with suction sampling in late June over 2 years (Reddersen, 1997):
Araneae (5.4–17.8 m−2 ), Collembola (0.65–155.9), Hemiptera
(14.1–2146 m−2 ), Hymenoptera (13.5–23.9 m−2 ), however much
more Coleoptera (51.5–110.4 m−2 ), Diptera (66.3–104.1 m−2 ),
and Lepidoptera (0.43 m−2 ) were reported. Similar to our study,
Moreby and Sotherton (1997) also found low abundances of
Diptera (5.4 m−2 ), Carabidae (0.82 m−2 ), and Chrysomelidae
(1.36 m−2 ) in conventional winter wheat fields in southern
England with suction samplings in June and July. Reasons for
differences in arthropod abundances in different studies reflect
climatic differences, effects of surrounding landscape structure,
influence of different insecticide usage or differences in wheat
varieties. The finding that mainly abundances but not diversity
was reduced suggests that the size of arthropod populations seem
to be the sensitive parameter responding to rainfall variations.
Whether effects of rainfall variations on arthropod abundances
have consequences on how fast arthropod populations can react
to environmental changes remains to be investigated by a specific experiment. We also found great differences in arthropod
abundances between sampling dates from April to June reflecting
the natural fluctuations due to different seasonal development of
the various arthropod taxa (Frampton et al., 2000; Afonina et al.,
2001; Abbas and Parwez, 2012).
ARTHROPOD ABUNDANCES AS INFLUENCED BY RAINFALL
Predicted rainfall variations reduced arthropod abundances
mainly in June but had little influence in April and May. We
explain this by the fact that rainfall treatments were established
only 1 month before the first arthropod sampling and by the relatively small difference between the rainfall scenarios in April and
May that may have been insufficient to cause shifts in arthropod
abundance. Moreover, until the first arthropod sampling in April
the prognosticated rainfall plots (38 mm) received even more
rainfall than the current rainfall plots (33 mm rainfall). Until the
second sampling date in May the current rainfall plots received
91 mm and the prognosticated rainfall plots 81 mm. Even, until
the June sampling the difference between the two rainfall treatments was only 20 mm, however rainfall amount combined with
extended dry periods was obviously enough to lead to several
significant differences in arthropod abundances. Moreover, the
increased soil matric potential in the prognosticated rainfall plots
showed that soil water was less available than under the current rainfall treatment affecting wheat biomass production and
weed abundance. Further, rainfall showed different effects on the
availability of water in different soil types as indicated by a significant interaction between rainfall and soil types for soil matric
potential.
October 2014 | Volume 2 | Article 44 | 7
Zaller et al.
FIGURE 6 | Abundance of Araneae (A), Coleoptera (B), Carabidea (C),
Chrysomelidae (D), Collembola (E), Diptera (F), Gastropoda (G),
Auchenorrhyncha (H) and Neuroptera (I) in winter wheat across the
Despite the small differences in rainfall it was interesting
to see significant differences in abundances of Gastropoda and
Auchenorrhyncha in April. However, given the small abundances of these taxa (0.31 m−2 for Gastropoda and 0.46 m−2 for
Auchenorrhyncha) results should be interpreted with caution. On
the other hand, the predicted rainfall plots received more precipitation than the current plots until April and Gastropoda are
known to be very sensitive to rainfall (Choi et al., 2004) and might
thus be sensitive indicators for changes in moisture. In our experiment Auchenorrhyncha (e.g., cicadas) also seemed to be sensitive
to rainfall, although others found no differences in the abundance in summer drought plots compared to plots under ambient
climate condition (Masters et al., 1998). Collembola, Diptera,
Neuroptera, and Saltatoria responded to rainfall scenarios in May.
This can be explained by a higher sensitivity to changes of these
four orders, so that small differences in rainfall amounts (9.8 mm)
and variation were effective, whereas the other orders appear to
be more tolerant against changes in rainfall. Others also found
that mites were not responsive to precipitation treatments, but
Collembola were (Kardol et al., 2011).
Frontiers in Environmental Science | Agroecology and Land Use Systems
Future rainfall and agroecosystem arthropods
three sampling dates (April, May, June) under current and predicted
rainfall at the soil types calcaric phaeozem (S), gleyic phaeozem (F) and
calcic chernozem (T). Means ± SE, n = 3.
In June 11 of the 18 arthropod groups investigated were
affected by rainfall treatments suggesting that 20 mm difference
in the amount of rainfall and 25% more dry days were enough
for these taxa to respond. Finding that certain arthropod taxa
were affected by rainfall treatments in 1 month but not in the
other (e.g., Gastropoda, Saltatoria) can be explained by spatial
and temporal variations of arthropod distribution between agroecosystems and the surrounding landscape (Afonina et al., 2001;
Tscharntke et al., 2002; Zaller et al., 2008b). Clearly, to better
understand the mechanisms underlying the relationship between
rainfall amounts/variations and arthropod abundances an analysis at the species level would be desirable. However, it can be
concluded from the current study that changes in rainfall variations with a slightly decreased amount of rainfall, more dry days
and more intensive rainfall events will most likely decrease the
abundance of aboveground arthropods in winter wheat crops.
Vegetation structural complexity, including crop biomass and
weed abundance which differed between the rainfall treatments,
is an important determinant of arthropod abundance and diversity in agroecosystems (Honek, 1988; Lagerlöf and Wallin, 1993;
October 2014 | Volume 2 | Article 44 | 8
Zaller et al.
Future rainfall and agroecosystem arthropods
Table 4 | Wheat height, wheat straw mass, weed abundance and
Table 5 | Correlation between arthropod abundance (June sampling)
biomass (all averaged across several sampling dates) in lysimeters
and straw and weed biomass (Pearson’s product-moment
cultivated with wheat in response to current vs. prognosticated
correlation).
rainfall variations and different soil types (S—calcaric phaeozem,
Straw biomass
F—gleyic phaeozem, T—calcic chernozem).
Parameter/Soil type
Treatments Current Rainfall
S soil
42.8 ± 0.7
46.3 ± 0.9
F soil
51.3 ± 0.3
48.9 ± 2.5
T soil
48.7 ± 0.7
50.9 ± 1.9
ANOVA RESULTS FOR WHEAT HEIGHT
Rainfall
P = 0.121
Soil types
P < 0.001
Rainfall × Soil types
P = 0.009
49.2 ± 1.1
54.8 ± 3.0
F soil
59.3 ± 0.5
57.2 ± 3.6
T soil
55.8 ± 1.1
61.4 ± 3.0
ANOVA RESULTS FOR WHEAT STRAW BIOMASS
P
0.240
0.576
0.012
0.120
0.635
0.517
0.028
Coleoptera
0.338
0.170
0.396
0.103
Collembola
0.168
0.505
0.542
0.020
−0.002
0.995
0.687
0.002
0.048
0.849
0.360
0.143
Hymenoptera
−0.181
0.471
−0.355
0.148
Not determinable
−0.013
0.960
0.607
0.008
0.381
0.119
0.340
0.167
−0.226
0.368
0.091
0.720
Diptera
Rare individuals
S soil
R
−0.292
Hemiptera
WHEAT STRAW BIOMASS (g m−2 )
P
Araneae
Acari
WHEAT HEIGHT (cm)
Saltatoria
Thysanoptera
0.135
0.593
0.745
<0.001
Total individuals
0.029
0.908
0.451
0.060
Significant correlations in bold.
P = 0.018
Rainfall
Soil types
P < 0.001
Rainfall × Soil types
P = 0.021
WEED ABUNDANCE (ind. m−2 )
S soil
345.8 ± 104.8
F soil
118.1 ± 48.9
87.5 ± 11.6
T soil
191.7 ± 50.6
156.9 ± 40.7
239.6 ± 68.1
ANOVA RESULTS FOR WEED ABUNDANCE
P = 0.070
Rainfall
Soil types
P < 0.001
Rainfall × Soil types
P = 0.503
WEED BIOMASS (g m−2 )
S soil
15.1 ± 3.2
18.9 ± 2.8
F soil
8.5 ± 5.8
8.8 ± 3.3
T soil
12.3 ± 3.7
9.6 ± 3.7
ANOVA RESULTS FOR WEED BIOMASS
Rainfall
P = 0.807
Soil types
P = 0.008
Rainfall × Soil types
P = 0.382
Means ± SD. Statistical results from Two-Way ANOVAs, significant effects are
in bold.
Frank and Nentwig, 1995; Kromp, 1999). Correlations between
arthropod abundance and crop and weed biomass suggest that
the rainfall effects indirectly affect arthropods by changes on
crops and weeds. Many studies describe the interrelation between
weeds and arthropods, in which greater weed density and diversity is associated with higher numbers of arthropods (Moreby
and Sotherton, 1997; Moreby and Southway, 1999; Marshall
et al., 2003). In the current study, 45% less weed biomass were
found in the predicted rainfall plots than in current plots and
thus the significant correlations for the abundance of arthropods (Acari, Araneae, Collembola, Diptera, and Thysanoptera)
and weed biomass are not surprising. However, it is somewhat
counterintuitive, that there was no correlation between numbers
of individuals of weeds and abundance of arthropods, except
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R
Progn. Rainfall
Weed biomass
for Hemiptera and total individuals in May. Also in contrast
to other studies is the lack of a correlation between arthropod abundance and crop height (Frampton et al., 2000; Perner
et al., 2005) indicating that our treatment factors rainfall and soil
types influenced relationships between arthropods and plants.
For example, the observed increased soil matric potential under
progn. rainfall suggests that crop and weed plants in these treatments had soil water less easily available than plants in curr.
rainfall treatments which could have affected the nutritional quality and structure of the crop-weed communities for arthropods
(Masters et al., 1998). Plant responses to soil water availability can
influence herbivore population dynamics with implications for
multitrophic arthropod-plant interactions (Masters et al., 1993;
Gange and Brown, 1997). Plant-mediated indirect effects of rainfall on arthropods have been described in detail for aphids where
the performance of aphids on drought-stressed relative to healthy
plants was increased, decreased or unchanged depending on the
aphid species, host-plant, timing and severity of the drought
stress (Pons and Tatchell, 1995). Whatever the causal mechanisms
are, the decrease in arthropod abundance can have potential
consequences for ecosystem function such as biological control,
nutrient cycling, pollination, seed dispersal, plant decomposition,
and soil alteration (Price, 1997; Bokhorst et al., 2008; Brantley
and Ford, 2012). Arthropods control populations of other organisms and provide a major food source for other taxa, like birds or
amphibia. Many farmland bird species are declining in Europe,
and one reason could be a decreasing availability of arthropods
(Moreby and Southway, 1999; Wilson et al., 1999; Hallmann
et al., 2014). Insects are also an important supplementary human
food source in many regions of the world, but as arthropods
can also cause damage through feeding injury or transmission of
plant-diseases, natural biological control in form of antagonistic
arthropods are crucial for agricultural systems worldwide (Foottit
and Adler, 2009). Our study also indicates that prognosticated
rainfall variations might have little influence on biological control
October 2014 | Volume 2 | Article 44 | 9
Zaller et al.
within the wheat agroecosystem as both important antagonists for
pests (Araneae, Carabidae) and pests themselves (Chrysomelidae,
Auchenorrhyncha) are reduced. However, the influence of rainfall on these pest-antagonist interactions demand more detailed
investigations.
When interpreting our data one has to keep in mind that in
climate change models temperature and precipitation are closely
linked. Since we only investigated rainfall effects while leaving
temperature unchanged, different impacts that the ones reported
here could occur when both factors, temperature and precipitation, are studied simultaneously.
Future rainfall and agroecosystem arthropods
studies investigating the combined effects of rainfall variations
and soil types on the abundance of aboveground arthropods,
more research is needed to get a better understanding of their
consequences on ecosystem functioning and services.
AUTHOR CONTRIBUTIONS
Laura Simmer, James Tabi Tataw, Johannes Hösch, Erwin Murer,
Johann G. Zaller conducted field work; Laura Simmer, Johann G.
Zaller, Nadja Santer, Erwin Murer analyzed the data; Johann G.
Zaller, Andreas Baumgarten, Herbert Formayer, Johannes Hösch,
Johann G. Zaller conceived and designed the experiment; all
authors wrote on the manuscript.
ARTHROPOD ABUNDANCE LITTLE INFLUENCED BY SOIL TYPES
Unlike expected, the soil types had no effect on arthropod abundances despite of clear differences in the availability of soil water
as measured by the soil matric potential. Surprisingly, also orders
which live in soil for most of its life cycle such as Collembola
did not respond to soil types and the availability of soil water
indicating that these taxa are rather tolerant to environmental conditions. As the factor soil type was rarely considered in
studies on arthropods there is little literature to compare with.
Differences in soil matric potential could also influence communities of soil bacteria and fungi and indirectly affect mycophagous
and detritivorous arthropod species; however this remains to be
investigated. Our current results of little influence of soil types on
arthropods are in contrast with those who found significant differences in the abundance of spiders, carabides and Heteroptera
in three different types of Estonian cultivated field soils; but there
was also no difference between soil types regarding the number of
Coleoptera (Ivask et al., 2008). When comparing those data one
has to keep in mind that in the former study pitfall traps were
used as opposed to suction sampling in the current study; moreover different times of the year in very different climatic regions
were studied. In our study soil types influenced wheat height
and weed abundance and the finding that some arthropod taxa
were correlated with vegetation density suggests some relationship (Chapman et al., 1999). However, other factors, including
competition between arthropod taxa from different trophic levels
(Perner et al., 2005) might have overruled possible effects of soil
types. In order to interpret these data in more detail, further studies investigating interactions between crop species and soil types
would be necessary.
CONCLUSION
Taken together, this study suggests that future rainfall variations
with less rainfall and longer drought periods during the vegetation period will significantly reduce the abundance of aboveground arthropods in winter wheat fields. The lack of significant
effects of soil types suggests that rainfall variations most likely
will have similar effects on different soil types. Weeds associated with winter wheat were shown to play an important role
in promoting arthropod abundance while effects of rainfall on
crop growth seemed to be of minor importance. The strong
response of arthropod abundances to only small differences in
rainfall amounts demands more appreciation of the effects of
rainfall variations when studying climate change effects on ecological interactions in agroecosystems. As this is among the first
Frontiers in Environmental Science | Agroecology and Land Use Systems
ACKNOWLEDGMENTS
We are grateful to Helene Berthold for providing logistical support during this project. Thanks to Karl Moder for statistical
advice. This research was funded by the Austrian Climate and
Energy Fund as part of the program ACRP2.
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Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 28 May 2014; accepted: 01 October 2014; published online: 20 October 2014.
Citation: Zaller JG, Simmer L, Santer N, Tabi Tataw J, Formayer H, Murer E, Hösch
J and Baumgarten A (2014) Future rainfall variations reduce abundances of aboveground arthropods in model agroecosystems with different soil types. Front. Environ.
Sci. 2:44. doi: 10.3389/fenvs.2014.00044
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